open-llm-leaderboard/details_lemon-mint__gemma-ko-7b-instruct-v0.50

该数据集是在模型 lemon-mint/gemma-ko-7b-instruct-v0.50 在 Open LLM Leaderboard 上的评估运行期间自动创建的。数据集由 63 个配置组成，每个配置对应一个评估任务。它包含 1 次运行的结果，每次运行作为每个配置中的一个特定分割存储。train 分割始终指向最新结果。一个名为 results 的额外配置存储了所有运行的聚合结果，这些结果用于计算和显示 Open LLM Leaderboard 上的聚合指标。README 还提供了一个示例，展示了如何使用 Python 中的 datasets 库加载运行中的详细信息。

原始信息汇总

数据集概述

该数据集是在评估模型 lemon-mint/gemma-ko-7b-instruct-v0.50 在 Open LLM Leaderboard 上的运行过程中自动创建的。

数据集组成

• 数据集包含 63 个配置，每个配置对应一个评估任务。
• 数据集从 1 次运行中创建，每次运行可以在每个配置中找到特定的分割，分割名称使用运行的时间戳。
• "train" 分割始终指向最新的结果。
• 一个额外的配置 "results" 存储所有运行的聚合结果，用于计算和显示 Open LLM Leaderboard 上的聚合指标。

数据加载示例

python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_lemon-mint__gemma-ko-7b-instruct-v0.50", "harness_winogrande_5", split="train")

最新结果

以下是 2024-04-03T01:16:41.111271 运行的最新结果：

python { "all": { "acc": 0.49337022225907295, "acc_stderr": 0.03438277644444711, "acc_norm": 0.4999963164047879, "acc_norm_stderr": 0.03517387854858427, "mc1": 0.24112607099143207, "mc1_stderr": 0.014974827279752329, "mc2": NaN, "mc2_stderr": NaN }, "harness|arc:challenge|25": { "acc": 0.431740614334471, "acc_stderr": 0.014474591427196206, "acc_norm": 0.48890784982935154, "acc_norm_stderr": 0.01460779491401305 }, "harness|hellaswag|10": { "acc": 0.5561641107349133, "acc_stderr": 0.004958201874334088, "acc_norm": 0.752141007767377, "acc_norm_stderr": 0.004308870978210411 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.33, "acc_stderr": 0.04725815626252605, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252605 }, "harness|hendrycksTest-anatomy|5": { "acc": 0.4444444444444444, "acc_stderr": 0.04292596718256981, "acc_norm": 0.4444444444444444, "acc_norm_stderr": 0.04292596718256981 }, "harness|hendrycksTest-astronomy|5": { "acc": 0.5526315789473685, "acc_stderr": 0.0404633688397825, "acc_norm": 0.5526315789473685, "acc_norm_stderr": 0.0404633688397825 }, "harness|hendrycksTest-business_ethics|5": { "acc": 0.49, "acc_stderr": 0.05024183937956911, "acc_norm": 0.49, "acc_norm_stderr": 0.05024183937956911 }, "harness|hendrycksTest-clinical_knowledge|5": { "acc": 0.569811320754717, "acc_stderr": 0.030471445867183235, "acc_norm": 0.569811320754717, "acc_norm_stderr": 0.030471445867183235 }, "harness|hendrycksTest-college_biology|5": { "acc": 0.4930555555555556, "acc_stderr": 0.04180806750294938, "acc_norm": 0.4930555555555556, "acc_norm_stderr": 0.04180806750294938 }, "harness|hendrycksTest-college_chemistry|5": { "acc": 0.33, "acc_stderr": 0.04725815626252604, "acc_norm": 0.33, "acc_norm_stderr": 0.04725815626252604 }, "harness|hendrycksTest-college_computer_science|5": { "acc": 0.37, "acc_stderr": 0.04852365870939099, "acc_norm": 0.37, "acc_norm_stderr": 0.04852365870939099 }, "harness|hendrycksTest-college_mathematics|5": { "acc": 0.34, "acc_stderr": 0.04760952285695235, "acc_norm": 0.34, "acc_norm_stderr": 0.04760952285695235 }, "harness|hendrycksTest-college_medicine|5": { "acc": 0.4913294797687861, "acc_stderr": 0.03811890988940412, "acc_norm": 0.4913294797687861, "acc_norm_stderr": 0.03811890988940412 }, "harness|hendrycksTest-college_physics|5": { "acc": 0.24509803921568626, "acc_stderr": 0.04280105837364395, "acc_norm": 0.24509803921568626, "acc_norm_stderr": 0.04280105837364395 }, "harness|hendrycksTest-computer_security|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-conceptual_physics|5": { "acc": 0.4425531914893617, "acc_stderr": 0.032469569197899575, "acc_norm": 0.4425531914893617, "acc_norm_stderr": 0.032469569197899575 }, "harness|hendrycksTest-econometrics|5": { "acc": 0.30701754385964913, "acc_stderr": 0.043391383225798615, "acc_norm": 0.30701754385964913, "acc_norm_stderr": 0.043391383225798615 }, "harness|hendrycksTest-electrical_engineering|5": { "acc": 0.45517241379310347, "acc_stderr": 0.04149886942192117, "acc_norm": 0.45517241379310347, "acc_norm_stderr": 0.04149886942192117 }, "harness|hendrycksTest-elementary_mathematics|5": { "acc": 0.32275132275132273, "acc_stderr": 0.024078943243597016, "acc_norm": 0.32275132275132273, "acc_norm_stderr": 0.024078943243597016 }, "harness|hendrycksTest-formal_logic|5": { "acc": 0.2777777777777778, "acc_stderr": 0.040061680838488774, "acc_norm": 0.2777777777777778, "acc_norm_stderr": 0.040061680838488774 }, "harness|hendrycksTest-global_facts|5": { "acc": 0.35, "acc_stderr": 0.04793724854411019, "acc_norm": 0.35, "acc_norm_stderr": 0.04793724854411019 }, "harness|hendrycksTest-high_school_biology|5": { "acc": 0.6096774193548387, "acc_stderr": 0.027751256636969576, "acc_norm": 0.6096774193548387, "acc_norm_stderr": 0.027751256636969576 }, "harness|hendrycksTest-high_school_chemistry|5": { "acc": 0.3891625615763547, "acc_stderr": 0.03430462416103872, "acc_norm": 0.3891625615763547, "acc_norm_stderr": 0.03430462416103872 }, "harness|hendrycksTest-high_school_computer_science|5": { "acc": 0.5, "acc_stderr": 0.050251890762960605, "acc_norm": 0.5, "acc_norm_stderr": 0.050251890762960605 }, "harness|hendrycksTest-high_school_european_history|5": { "acc": 0.5878787878787879, "acc_stderr": 0.03843566993588717, "acc_norm": 0.5878787878787879, "acc_norm_stderr": 0.03843566993588717 }, "harness|hendrycksTest-high_school_geography|5": { "acc": 0.6717171717171717, "acc_stderr": 0.03345678422756776, "acc_norm": 0.6717171717171717, "acc_norm_stderr": 0.03345678422756776 }, "harness|hendrycksTest-high_school_government_and_politics|5": { "acc": 0.6839378238341969, "acc_stderr": 0.033553973696861736, "acc_norm": 0.6839378238341969, "acc_norm_stderr": 0.033553973696861736 }, "harness|hendrycksTest-high_school_macroeconomics|5": { "acc": 0.48205128205128206, "acc_stderr": 0.02533466708095495, "acc_norm": 0.48205128205128206, "acc_norm_stderr": 0.02533466708095495 }, "harness|hendrycksTest-high_school_mathematics|5": { "acc": 0.24814814814814815, "acc_stderr": 0.0263357394040558, "acc_norm": 0.24814814814814815, "acc_norm_stderr": 0.026335739